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A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Author

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  • Xin Chen

    (School of Medicine, Hangzhou Normal University, Hangzhou 311121, China
    Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
    Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China)

  • Zhigeng Pan

    (School of Medicine, Hangzhou Normal University, Hangzhou 311121, China
    Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
    Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China)

Abstract

Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.

Suggested Citation

  • Xin Chen & Zhigeng Pan, 2021. "A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health," IJERPH, MDPI, vol. 18(12), pages 1-12, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6441-:d:574811
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    References listed on IDEAS

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    1. Juan Carlos Sánchez-García & Jonathan Cortés-Martín & Raquel Rodríguez-Blanque & Ana Eugenia Marín-Jiménez & Maria Montiel-Troya & Lourdes Díaz-Rodríguez, 2021. "Depression and Anxiety in Patients with Rare Diseases during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(6), pages 1-10, March.
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    Cited by:

    1. Sungchul Mun & Sangin Park & Sungyop Whang & Mincheol Whang, 2022. "Effects of Temporary Respiration Exercise with Individual Harmonic Frequency on Blood Pressure and Autonomic Balance," IJERPH, MDPI, vol. 19(23), pages 1-19, November.
    2. Xin Chen & Liangwen Xu & Zhigeng Pan, 2022. "Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression," IJERPH, MDPI, vol. 19(6), pages 1-12, March.

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